1. Field of the Invention
The exemplary embodiment(s) of the present invention relates to a field of decoder for underdetermined MIMO systems. More specifically, the exemplary embodiment(s) of the present invention relates to a geometry based efficient decoder for underdetermined MIMO systems and a decoding method thereof.
2. Description of Related Art
Recently, in order to satisfy the growing demands of the personal communications, the design of next generation wireless communication systems goes for supporting high data rate and high mobility. However, the link quality suffers from frequency selective and time selective fading caused by multipath propagation in wireless channels. Moreover, the quality and reliability of wireless communication are degraded by Doppler shift and carrier frequency/phase. Beside, due to the limited available bandwidth and transmitted power, the design challenge of wireless communication systems becomes more difficult. Therefore, many innovative techniques have been devised and extensively used in this field to improve the reliability and the spectral efficiency of wireless communication links e.g. the coded multicarrier modulation, smart antenna and multiple-input multiple-output (MIMO) technology and adaptive modulation.
Among these technologies, MIMO is the most outstanding one. MIMO technology involves the use of multiple antennas to improve link performance. There are two major features of MIMO technologies: spatial multiplexing for increasing data rate and spatial diversity for improve link quality. Spatial multiplexing offers a linear increasing of data rate by transmitting multiple independent data streams at the same time. Spatial diversity provides diversity gain to mitigate fading effects by using the multiple (ideally independent) copies of the transmitted signal in space, time and frequency. They are usually trade-offs to each other and provide an effective and promising solution while achieving high-data rate and reliable transmission.
The major MIMO signal detection schemes include linear detection, successive interference cancellation (SIC) and the maximum-likelihood (ML) detection. The advantages of the first two detection schemes are low decoding complexity and easy implementation but their detection performances are non-optimal. ML detection provides optimal detection performance but its complexity increases exponentially with the size of constellation and the number of transmit antennas. Therefore, the design of high-performance and low decoding complexity is the one of key issues of MIMO designs. To reduce the complexity of the ML detector, the sphere decoding algorithm (SDA) has received considerable attention as an efficient detection scheme for MIMO systems. However, typical SDA fail to work in underdetermined MIMO systems where the number of transmit antennas is larger than the number of receive antennas.
To overcome the above drawbacks of typical SDA, the conventional generalized sphere decoder (GSD), double-layer sphere decoder (DLSD) and slab sphere decoder (SSD) are introduced. These decoders transform underdetermined systems into overdetermined systems that can be solved by the SDA. Since the GSD performs an exhaustive search on (Nt-Nr) dimensions for the ML solution, the decoding complexity is increasing with the size of constellation and the antenna number difference. The DLSD uses the outer sphere decoder to find the valid candidate points and then the inner sphere decoder uses those points to find the solution. The SSD uses the geometry concept to find the valid candidate points to reduce the searching complexity of DLSD. However, the SDA still needs to be performed too many times in SSD.